488 research outputs found

    Respiratory Sound Analysis for the Evidence of Lung Health

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    Significant changes have been made on audio-based technologies over years in several different fields along with healthcare industry. Analysis of Lung sounds is a potential source of noninvasive, quantitative information along with additional objective on the status of the pulmonary system. To do that medical professionals listen to sounds heard over the chest wall at different positions with a stethoscope which is known as auscultation and is important in diagnosing respiratory diseases. At times, possibility of inaccurate interpretation of respiratory sounds happens because of clinician’s lack of considerable expertise or sometimes trainees such as interns and residents misidentify respiratory sounds. We have built a tool to distinguish healthy respiratory sound from non-healthy ones that come from respiratory infection carrying patients. The audio clips were characterized using Linear Predictive Cepstral Coefficient (LPCC)-based features and the highest possible accuracy of 99.22% was obtained with a Multi-Layer Perceptron (MLP)- based classifier on the publicly available ICBHI17 respiratory sounds dataset [1] of size 6800+ clips. The system also outperformed established works in literature and other machine learning techniques. In future we will try to use larger dataset with other acoustic techniques along with deep learning-based approaches and try to identify the nature and severity of infection using respiratory sounds

    Telemedicine

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    Telemedicine is a rapidly evolving field as new technologies are implemented for example for the development of wireless sensors, quality data transmission. Using the Internet applications such as counseling, clinical consultation support and home care monitoring and management are more and more realized, which improves access to high level medical care in underserved areas. The 23 chapters of this book present manifold examples of telemedicine treating both theoretical and practical foundations and application scenarios

    Towards a tricorder: clinical, health economic, and ethical investigation of point-of-care artificial intelligence electrocardiogram for heart failure

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    Heart failure (HF) is an international public health priority and a focus of the NHS Long Term Plan. There is a particular need in primary care for screening and early detection of heart failure with reduced ejection fraction (HFrEF) – the most common and serious HF subtype, and the only one with an abundant evidence base for effective therapies. Digital health technologies (DHTs) integrating artificial intelligence (AI) could improve diagnosis of HFrEF. Specifically, through a convergence of DHTs and AI, a single-lead electrocardiogram (ECG) can be recorded by a smart stethoscope and interrogated by AI (AI-ECG) to potentially serve as a point-of-care HFrEF test. However, there are concerning evidence gaps for such DHTs applying AI; across intersecting clinical, health economic, and ethical considerations. My thesis therefore investigates hypotheses that AI-ECG is 1.) Reliable, accurate, unbiased, and can be patient self-administered, 2.) Of justifiable health economic impact for primary care deployment, and 3.) Appropriate across ethical domains for deployment as a tool for patient self-administered screening. The theoretical basis for this work is presented in the Introduction (Chapter 1). Chapter 2 describes the first large-scale, multi-centre independent external validation study of AI-ECG, prospectively recruiting 1,050 patients and highlighting impressive performance: area under the curve, sensitivity, and specificity up to 0·91 (95% confidence interval: 0·88–0·95), 91·9% (78·1–98·3), and 80·2% (75·5–84·3) respectively; and absence of bias by age, sex, and ethnicity. Performance was independent of operator, and usability of the tool extended to patients being able to self-examine. Chapter 3 presents a clinical and health economic outcomes analysis using a contemporary digital repository of 2.5 million NHS patient records. A propensity-matched cohort was derived using all patients diagnosed with HF from 2015-2020 (n = 34,208). Novel findings included the unacceptable reality that 70% of index HF diagnoses are made through hospitalisation; where index diagnosis through primary care conferred a medium-term survival advantage and long-term cost saving (£2,500 per patient). This underpins a health economic model for the deployment of AI-ECG across primary care. Chapter 4 approaches a normative ethical analysis focusing on equity, agency, data rights, and responsibility for safe, effective, and trustworthy implementation of an unprecedented at-home patient self-administered AI-ECG screening programme. I propose approaches to mitigating any potential harms, towards preserving and promoting trust, patient engagement, and public health. Collectively, this thesis marks novel work highlighting AI-ECG as tool with the potential to address major cardiovascular public health priorities. Scrutiny through complimentary clinical, health economic, and ethical considerations can directly serve patients and health systems by blueprinting best-practice for the evaluation and implementation of DHTs integrating AI – building the conviction needed to realise the full potential of such technologies.Open Acces

    Processing and analysis of foetal phonocardiographic signals

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    A framework for automated heart and lung sound analysis using a mobile telemedicine platform

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    Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 246-261).Many resource-poor communities across the globe lack access to quality healthcare,due to shortages in medical expertise and poor availability of medical diagnostic devices. In recent years, mobile phones have become increasingly complex and ubiquitous. These devices present a tremendous opportunity to provide low-cost diagnostics to under-served populations and to connect non-experts with experts. This thesis explores the capture of cardiac and respiratory sounds on a mobile phone for analysis, with the long-term aim of developing intelligent algorithms for the detection of heart and respiratory-related problems. Using standard labeled databases, existing and novel algorithms are developed to analyze cardiac and respiratory audio data. In order to assess the algorithms' performance under field conditions, a low-cost stethoscope attachment is constructed and data is collected using a mobile phone. Finally, a telemedicine infrastructure and work-flow is described, in which these algorithms can be deployed and trained in a large-scale deployment.by Katherine L. Kuan.M.Eng

    PRESENT AND FUTURE PERVASIVE HEALTHCARE METHODOLOGIES: INTELLIGENT BODY DEVICES, PROCESSING AND MODELING TO SEARCH FOR NEW CARDIOVASCULAR AND PHYSIOLOGICAL BIOMARKERS

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    The motivation behind this work comes from the area of pervasive computing technologies for healthcare and wearable healthcare IT systems, an emerging field of research that brings in revolutionary paradigms for computing models in the 21st century. The aim of this thesis is focused on emerging personal health technologies and pattern recognition strategies for early diagnosis and personalized treatment and rehabilitation for individuals with cardiovascular and neurophysiological diseases. Attention was paid to the development of an intelligent system for the automatic classification of cardiac valve disease for screening purposes. Promising results were reported with the possibility to implement a new screening strategy for the diagnosis of cardiac valve disease in developing countries. A novel assistive architecture for the elderly able to non-invasively assess muscle fatigue by surface electromyography using wireless platform during exercise with an ergonomic platform was presented. Finally a wearable chest belt for ECG monitoring to investigate the psycho-physiological effects of the autonomic system and a wearable technology for monitoring of knee kinematics and recognition of ambulatory activities were characterized to evaluate the reliability for clinical purposes of collected data. The potential impact in the clinical arena of this research would be extremely important, since promising data show how such emerging personal technologies and methodologies are effective in several scenarios to early screening and discovery of novel diagnostic and prognostic biomarkers

    On the development of intelligent medical systems for pre-operative anaesthesia assessment

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    This thesis describes the research and development of a decision support tool for determining a medical patient's suitability for surgical anaesthesia. At present, there is a change in the way that patients are clinically assessedp rior to surgery. The pre-operative assessment, usually conducted by a qualified anaesthetist, is being more frequently performed by nursing grade staff. The pre-operative assessmenet xists to minimise the risk of surgical complications for the patient. Nursing grade staff are often not as experienced as qualified anaesthetists, and thus are not as well suited to the role of performing the pre-operative assessment. This research project used data collected during pre-operative assessments to develop a decision support tool that would assist the nurse (or anaesthetist) in determining whether a patient is suitable for surgical anaesthesia. The three main objectives are: firstly, to research and develop an automated intelligent systems technique for classifying heart and lung sounds and hence identifying cardio-respiratory pathology. Secondly, to research and develop an automated intelligent systems technique for assessing the patient's blood oxygen level and pulse waveform. Finally, to develop a decision support tool that would combine the assessmentsa bove in forming a decision as to whether the patient is suitable for surgical anaesthesia. Clinical data were collected from hospital outpatient departments and recorded alongside the diagnoses made by a qualified anaesthetist. Heart and lung sounds were collected using an electronic stethoscope. Using this data two ensembles of artificial neural networks were trained to classify the different heart and lung sounds into different pathology groups. Classification accuracies up to 99.77% for the heart sounds, and 100% for the lung sounds has been obtained. Oxygen saturation and pulse waveform measurements were recorded using a pulse oximeter. Using this data an artificial neural network was trained to discriminate between normal and abnormal pulse waveforms. A discrimination accuracy of 98% has been obtained from the system. A fuzzy inference system was generated to classify the patient's blood oxygen level as being either an inhibiting or non-inhibiting factor in their suitability for surgical anaesthesia. When tested the system successfully classified 100% of the test dataset. A decision support tool, applying the genetic programming evolutionary technique to a fuzzy classification system was created. The decision support tool combined the results from the heart sound, lung sound and pulse oximetry classifiers in determining whether a patient was suitable for surgical anaesthesia. The evolved fuzzy system attained a classification accuracy of 91.79%. The principal conclusion from this thesis is that intelligent systems, such as artificial neural networks, genetic programming, and fuzzy inference systems, can be successfully applied to the creation of medical decision support tools.EThOS - Electronic Theses Online ServiceMedicdirect.co.uk Ltd.GBUnited Kingdo

    AI Knowledge Transfer from the University to Society

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    AI Knowledge Transfer from the University to Society: Applications in High-Impact Sectors brings together examples from the "Innovative Ecosystem with Artificial Intelligence for Andalusia 2025" project at the University of Seville, a series of sub-projects composed of research groups and different institutions or companies that explore the use of Artificial Intelligence in a variety of high-impact sectors to lead innovation and assist in decision-making. Key Features Includes chapters on health and social welfare, transportation, digital economy, energy efficiency and sustainability, agro-industry, and tourism Great diversity of authors, expert in varied sectors, belonging to powerful research groups from the University of Seville with proven experience in the transfer of knowledge to the productive sector and agents attached to the Andalucía TECH Campu

    The role of technology in reducing health care costs. Phase II and phase III.

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